LICK INDICES and SPECTRAL ENERGY DISTRIBUTION ANALYSIS BASED on AN M31 STAR CLUSTER SAMPLE: COMPARISONS of METHODS and MODELS

Application of fitting techniques to obtain physical parameters—such as ages, metallicities, and α-element to iron ratios—of stellar populations is an important approach to understanding the nature of both galaxies and globular clusters (GCs). In fact, fitting methods based on different underlying models may yield different results and with varying precision. In this paper, we have selected 22 confirmed M31 GCs for which we do not have access to previously known spectroscopic metallicities. Most are located at approximately one degree (in projection) from the galactic center. We performed spectroscopic observations with the 6.5 m MMT telescope, equipped with its Red Channel Spectrograph. Lick/IDS absorption-line indices, radial velocities, ages, and metallicities were derived based on the EZ_Ages stellar population parameter calculator. We also applied full spectral fitting with the ULySS code to constrain the parameters of our sample star clusters. In addition, we performed ${\chi }_{\min }^{2}$ fitting of the clusters' Lick/IDS indices with different models, including the Bruzual & Charlot models (adopting Chabrier or Salpeter stellar initial mass functions and 1994 or 2000 Padova stellar evolutionary tracks), the galev, and the Thomas et al. models. For comparison, we collected their UVBRIJK photometry from the Revised Bologna Catalogue (v.5) to obtain and fit the GCs' spectral energy distributions (SEDs). Finally, we performed fits using a combination of Lick/IDS indices and SEDs. The latter results are more reliable and the associated error bars become significantly smaller than those resulting from either our Lick/IDS indices-only or our SED-only fits

Reinforcement Learning for Vehicle Route Optimization in SUMO

Urban traffic control becomes a major topic for urban development lately as the growing number of vehicles in the transportation network. Recent advances in reinforcement learning methodologies have shown highly potential results in solving complex traffic control problem with multi-dimensional states and actions. It offers an opportunity to build a sustainable and resilient urban transport network for a variety of objects, such as minimizing the fuel consumption or improving the safety of roadway. Inspired by this promising idea, this paper presents an experience how to apply reinforcement learning method to optimize the route of a single vehicle in a network. This experience uses an open-source simulator SUMO to simulate the traffic. It shows promising result in finding the best route and avoiding the congestion path

Real-time deep reinforcement learning based vehicle navigation

Traffic congestion has become one of the most serious contemporary city issues as it leads to unnecessary high energy consumption, air pollution and extra traveling time. During the past decade, many optimization algorithms have been designed to achieve the optimal usage of existing roadway capacity in cities to leverage the problem. However, it is still a challenging task for the vehicles to interact with the complex city environment in a real time manner. In this paper, we propose a deep reinforcement learning (DRL) method to build a real-time intelligent vehicle routing and navigation system by formulating the task as a sequence of decisions. In addition, an integrated framework is provided to facilitate the intelligent vehicle navigation research by embedding smart agents into the SUMO simulator. Nine realistic traffic scenarios are simulated to test the proposed navigation method. The experimental results have demonstrated the efficient convergence of the vehicle navigation agents and their effectiveness to make optimal decisions under the volatile traffic conditions. The results also show that the proposed method provides a better navigation solution comparing to the benchmark routing optimization algorithms. The performance has been further validated by using the Wilcoxon test. It is found that the achieved improvement of our proposed method becomes more significant under the maps with more edges (roads) and more complicated traffics comparing to the state-of-the-art navigation methods